{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.10"
    },
    "colab": {
      "name": "FinRL_single_stock_trading.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "Y9CM5DeIr9GC",
        "9upN8FI2r_X1",
        "1sve9WGvsC__",
        "oDk2qrlTLZCp"
      ],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-LLC/FinRL-Library/blob/master/FinRL_single_stock_trading.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Idd1jem0TnST"
      },
      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Single Stock Trading\n",
        "\n",
        "Tutorials to use OpenAI DRL to trade single stock in one Jupyter Notebook | Presented at NeurIPS 2020: Deep RL Workshop\n",
        "\n",
        "* This blog is based on our paper: FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, presented at NeurIPS 2020: Deep RL Workshop.\n",
        "* Check out medium blog for detailed explanations: https://towardsdatascience.com/finrl-for-quantitative-finance-tutorial-for-single-stock-trading-37d6d7c30aac\n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vglc_9N5-KZ"
      },
      "source": [
        "## Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ex2ord116AbP"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1eLhMW36cLi"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3eimeRv06YoK"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
        "\n",
        "The components of the reinforcement learning environment are:\n",
        "\n",
        "\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
        "\n",
        "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
        "values at state s′ and s, respectively\n",
        "\n",
        "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
        "our trading agent observes many different features to better learn in an interactive environment.\n",
        "\n",
        "* Environment: single stock trading for AAPL\n",
        "\n",
        "\n",
        "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n",
        "\n",
        "We use Apple Inc. stock: AAPL as an example throughout this article, because it is one of the most popular and profitable stocks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xD3f90UnTnSU"
      },
      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBUcBKap-oII"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install all the packages through FinRL library\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "40axJBAP5mer",
        "outputId": "67686ede-b511-4eb5-959c-8a2be3a26f9f"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/AI4Finance-LLC/FinRL-Library.git\n",
            "  Cloning https://github.com/AI4Finance-LLC/FinRL-Library.git to /tmp/pip-req-build-xjk7w6qh\n",
            "  Running command git clone -q https://github.com/AI4Finance-LLC/FinRL-Library.git /tmp/pip-req-build-xjk7w6qh\n",
            "Requirement already satisfied: numpy<1.19.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from finrl==0.0.1) (1.18.5)\n",
            "Requirement already satisfied: pandas==1.1.4 in /usr/local/lib/python3.6/dist-packages (from finrl==0.0.1) (1.1.4)\n",
            "Collecting stockstats\n",
            "  Downloading https://files.pythonhosted.org/packages/32/41/d3828c5bc0a262cb3112a4024108a3b019c183fa3b3078bff34bf25abf91/stockstats-0.3.2-py2.py3-none-any.whl\n",
            "Collecting yfinance\n",
            "  Downloading https://files.pythonhosted.org/packages/7a/e8/b9d7104d3a4bf39924799067592d9e59119fcfc900a425a12e80a3123ec8/yfinance-0.1.55.tar.gz\n",
            "Collecting scikit-learn==0.21.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b7/6c/ec121123c671d980c6969dfc69d0f09e1d7f88d80d373f511e61d773b85c/scikit_learn-0.21.0-cp36-cp36m-manylinux1_x86_64.whl (6.6MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6MB 4.8MB/s \n",
            "\u001b[?25hCollecting gym==0.15.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d2/88/a7186ffe1f33570ad3b8cd635996e5a3e3e155736e180ae6a2ad5e826a60/gym-0.15.3.tar.gz (1.6MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6MB 36.2MB/s \n",
            "\u001b[?25hCollecting stable-baselines[mpi]\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/48/d428b79bd4360727925f9fe34afeea7a9da381da3dc8748df834a349ad1d/stable_baselines-2.10.1-py3-none-any.whl (240kB)\n",
            "\u001b[K     |████████████████████████████████| 245kB 39.7MB/s \n",
            "\u001b[?25hCollecting tensorflow==1.15.4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8e/64/7a19837dd54d3f53b1ce5ae346ab401dde9678e8f233220317000bfdb3e2/tensorflow-1.15.4-cp36-cp36m-manylinux2010_x86_64.whl (110.5MB)\n",
            "\u001b[K     |████████████████████████████████| 110.5MB 47kB/s \n",
            "\u001b[?25hCollecting joblib==0.15.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b8/a6/d1a816b89aa1e9e96bcb298eb1ee1854f21662ebc6d55ffa3d7b3b50122b/joblib-0.15.1-py3-none-any.whl (298kB)\n",
            "\u001b[K     |████████████████████████████████| 307kB 44.6MB/s \n",
            "\u001b[?25hCollecting matplotlib==3.2.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/93/4b/52da6b1523d5139d04e02d9e26ceda6146b48f2a4e5d2abfdf1c7bac8c40/matplotlib-3.2.1-cp36-cp36m-manylinux1_x86_64.whl (12.4MB)\n",
            "\u001b[K     |████████████████████████████████| 12.4MB 23.1MB/s \n",
            "\u001b[?25hCollecting pytest<6.0.0,>=5.3.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9f/f3/0a83558da436a081344aa6c8b85ea5b5f05071214106036ce341b7769b0b/pytest-5.4.3-py3-none-any.whl (248kB)\n",
            "\u001b[K     |████████████████████████████████| 256kB 38.3MB/s \n",
            "\u001b[?25hCollecting setuptools<42.0.0,>=41.4.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d9/de/554b6310ac87c5b921bc45634b07b11394fe63bc4cb5176f5240addf18ab/setuptools-41.6.0-py2.py3-none-any.whl (582kB)\n",
            "\u001b[K     |████████████████████████████████| 583kB 45.0MB/s \n",
            "\u001b[?25hCollecting wheel<0.34.0,>=0.33.6\n",
            "  Downloading https://files.pythonhosted.org/packages/00/83/b4a77d044e78ad1a45610eb88f745be2fd2c6d658f9798a15e384b7d57c9/wheel-0.33.6-py2.py3-none-any.whl\n",
            "Collecting pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2\n",
            "  Cloning https://github.com/quantopian/pyfolio.git to /tmp/pip-install-7woxk6he/pyfolio\n",
            "  Running command git clone -q https://github.com/quantopian/pyfolio.git /tmp/pip-install-7woxk6he/pyfolio\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas==1.1.4->finrl==0.0.1) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas==1.1.4->finrl==0.0.1) (2.8.1)\n",
            "Collecting int-date>=0.1.7\n",
            "  Downloading https://files.pythonhosted.org/packages/43/27/31803df15173ab341fe7548c14154b54227dfd8f630daa09a1c6e7db52f7/int_date-0.1.8-py2.py3-none-any.whl\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from yfinance->finrl==0.0.1) (2.23.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from yfinance->finrl==0.0.1) (0.0.9)\n",
            "Collecting lxml>=4.5.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bd/78/56a7c88a57d0d14945472535d0df9fb4bbad7d34ede658ec7961635c790e/lxml-4.6.2-cp36-cp36m-manylinux1_x86_64.whl (5.5MB)\n",
            "\u001b[K     |████████████████████████████████| 5.5MB 38.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn==0.21.0->finrl==0.0.1) (1.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gym==0.15.3->finrl==0.0.1) (1.15.0)\n",
            "Collecting pyglet<=1.3.2,>=1.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1c/fc/dad5eaaab68f0c21e2f906a94ddb98175662cc5a654eee404d59554ce0fa/pyglet-1.3.2-py2.py3-none-any.whl (1.0MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0MB 28.6MB/s \n",
            "\u001b[?25hCollecting cloudpickle~=1.2.0\n",
            "  Downloading https://files.pythonhosted.org/packages/c1/49/334e279caa3231255725c8e860fa93e72083567625573421db8875846c14/cloudpickle-1.2.2-py2.py3-none-any.whl\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.6/dist-packages (from stable-baselines[mpi]->finrl==0.0.1) (4.1.2.30)\n",
            "Collecting mpi4py; extra == \"mpi\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ec/8f/bbd8de5ba566dd77e408d8136e2bab7fdf2b97ce06cab830ba8b50a2f588/mpi4py-3.0.3.tar.gz (1.4MB)\n",
            "\u001b[K     |████████████████████████████████| 1.4MB 40.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (0.8.1)\n",
            "Collecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (1.12.1)\n",
            "Collecting tensorflow-estimator==1.15.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/de/62/2ee9cd74c9fa2fa450877847ba560b260f5d0fb70ee0595203082dafcc9d/tensorflow_estimator-1.15.1-py2.py3-none-any.whl (503kB)\n",
            "\u001b[K     |████████████████████████████████| 512kB 31.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (1.1.0)\n",
            "Collecting tensorboard<1.16.0,>=1.15.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1e/e9/d3d747a97f7188f48aa5eda486907f3b345cd409f0a0850468ba867db246/tensorboard-1.15.0-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 39.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (0.10.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (1.33.2)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (0.2.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (3.12.4)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (1.1.2)\n",
            "Collecting keras-applications>=1.0.8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 5.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (3.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib==3.2.1->finrl==0.0.1) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib==3.2.1->finrl==0.0.1) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib==3.2.1->finrl==0.0.1) (2.4.7)\n",
            "Collecting pluggy<1.0,>=0.12\n",
            "  Downloading https://files.pythonhosted.org/packages/a0/28/85c7aa31b80d150b772fbe4a229487bc6644da9ccb7e427dd8cc60cb8a62/pluggy-0.13.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pytest<6.0.0,>=5.3.2->finrl==0.0.1) (8.6.0)\n",
            "Requirement already satisfied: importlib-metadata>=0.12; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from pytest<6.0.0,>=5.3.2->finrl==0.0.1) (2.0.0)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from pytest<6.0.0,>=5.3.2->finrl==0.0.1) (1.9.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from pytest<6.0.0,>=5.3.2->finrl==0.0.1) (20.4)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.6/dist-packages (from pytest<6.0.0,>=5.3.2->finrl==0.0.1) (20.3.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from pytest<6.0.0,>=5.3.2->finrl==0.0.1) (0.2.5)\n",
            "Requirement already satisfied: ipython>=3.2.3 in /usr/local/lib/python3.6/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (5.5.0)\n",
            "Requirement already satisfied: seaborn>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (0.11.0)\n",
            "Collecting empyrical>=0.5.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/74/43/1b997c21411c6ab7c96dc034e160198272c7a785aeea7654c9bcf98bec83/empyrical-0.5.5.tar.gz (52kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 7.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance->finrl==0.0.1) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance->finrl==0.0.1) (2020.11.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance->finrl==0.0.1) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance->finrl==0.0.1) (1.24.3)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyglet<=1.3.2,>=1.2.0->gym==0.15.3->finrl==0.0.1) (0.16.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.4->finrl==0.0.1) (3.3.3)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.4->finrl==0.0.1) (1.0.1)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==1.15.4->finrl==0.0.1) (2.10.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.12; python_version < \"3.8\"->pytest<6.0.0,>=5.3.2->finrl==0.0.1) (3.4.0)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (0.7.5)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (1.0.18)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (4.3.3)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (4.4.2)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (0.8.1)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (2.6.1)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (4.8.0)\n",
            "Requirement already satisfied: pandas-datareader>=0.2 in /usr/local/lib/python3.6/dist-packages (from empyrical>=0.5.0->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (0.9.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (0.2.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (0.6.0)\n",
            "Building wheels for collected packages: finrl, yfinance, gym, pyfolio, mpi4py, gast, empyrical\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.0.1-cp36-none-any.whl size=24307 sha256=d10143602292caf5d185b751192f43b14aa66d057256c6b2b3fdf0e469e25e19\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-1wh2vd75/wheels/9c/19/bf/c644def96612df1ad42c94d5304966797eaa3221dffc5efe0b\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.55-py2.py3-none-any.whl size=22618 sha256=121273564ef1d0b1f24b303e4d82e5461b880bcf52b1a2bc065f74bc3d1f2fb8\n",
            "  Stored in directory: /root/.cache/pip/wheels/04/98/cc/2702a4242d60bdc14f48b4557c427ded1fe92aedf257d4565c\n",
            "  Building wheel for gym (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gym: filename=gym-0.15.3-cp36-none-any.whl size=1644971 sha256=13f5b1331556a5803f9f49164ec821ee358d44bd155b6368035de000fc3f409d\n",
            "  Stored in directory: /root/.cache/pip/wheels/8a/71/10/30f9b16332ecfd6318ac290445c696fe809bcbe40a05f9a799\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp36-none-any.whl size=75764 sha256=790d0e786fe151fb75844c797bf0f34a72f6c171f9b4ce14b82df7119e4cadb0\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-1wh2vd75/wheels/43/ce/d9/6752fb6e03205408773235435205a0519d2c608a94f1976e56\n",
            "  Building wheel for mpi4py (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mpi4py: filename=mpi4py-3.0.3-cp36-cp36m-linux_x86_64.whl size=2074492 sha256=1d433db55ed86c0d9b3c92160789ac7d4c888e446538ef6ee103a5788171bfd0\n",
            "  Stored in directory: /root/.cache/pip/wheels/18/e0/86/2b713dd512199096012ceca61429e12b960888de59818871d6\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7542 sha256=f45be249747a4f1cebfb630cf5ec81dd33ec92c514479394aacd7092fbc9c6ec\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-cp36-none-any.whl size=39763 sha256=30bb03ce53ffb100c8979e252d42a2b8a9822e2ebf11ac11528973cc6a2e3e9e\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
            "Successfully built finrl yfinance gym pyfolio mpi4py gast empyrical\n",
            "\u001b[31mERROR: tensorflow-probability 0.11.0 has requirement cloudpickle==1.3, but you'll have cloudpickle 1.2.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow-probability 0.11.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: int-date, stockstats, lxml, yfinance, joblib, scikit-learn, pyglet, cloudpickle, gym, matplotlib, mpi4py, stable-baselines, gast, tensorflow-estimator, setuptools, wheel, tensorboard, keras-applications, tensorflow, pluggy, pytest, empyrical, pyfolio, finrl\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "  Found existing installation: joblib 0.17.0\n",
            "    Uninstalling joblib-0.17.0:\n",
            "      Successfully uninstalled joblib-0.17.0\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "  Found existing installation: pyglet 1.5.0\n",
            "    Uninstalling pyglet-1.5.0:\n",
            "      Successfully uninstalled pyglet-1.5.0\n",
            "  Found existing installation: cloudpickle 1.3.0\n",
            "    Uninstalling cloudpickle-1.3.0:\n",
            "      Successfully uninstalled cloudpickle-1.3.0\n",
            "  Found existing installation: gym 0.17.3\n",
            "    Uninstalling gym-0.17.3:\n",
            "      Successfully uninstalled gym-0.17.3\n",
            "  Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "  Found existing installation: tensorflow-estimator 2.3.0\n",
            "    Uninstalling tensorflow-estimator-2.3.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.3.0\n",
            "  Found existing installation: setuptools 50.3.2\n",
            "    Uninstalling setuptools-50.3.2:\n",
            "      Successfully uninstalled setuptools-50.3.2\n",
            "  Found existing installation: wheel 0.35.1\n",
            "    Uninstalling wheel-0.35.1:\n",
            "      Successfully uninstalled wheel-0.35.1\n",
            "  Found existing installation: tensorboard 2.3.0\n",
            "    Uninstalling tensorboard-2.3.0:\n",
            "      Successfully uninstalled tensorboard-2.3.0\n",
            "  Found existing installation: tensorflow 2.3.0\n",
            "    Uninstalling tensorflow-2.3.0:\n",
            "      Successfully uninstalled tensorflow-2.3.0\n",
            "  Found existing installation: pluggy 0.7.1\n",
            "    Uninstalling pluggy-0.7.1:\n",
            "      Successfully uninstalled pluggy-0.7.1\n",
            "  Found existing installation: pytest 3.6.4\n",
            "    Uninstalling pytest-3.6.4:\n",
            "      Successfully uninstalled pytest-3.6.4\n",
            "Successfully installed cloudpickle-1.2.2 empyrical-0.5.5 finrl-0.0.1 gast-0.2.2 gym-0.15.3 int-date-0.1.8 joblib-0.15.1 keras-applications-1.0.8 lxml-4.6.2 matplotlib-3.2.1 mpi4py-3.0.3 pluggy-0.13.1 pyfolio-0.9.2+75.g4b901f6 pyglet-1.3.2 pytest-5.4.3 scikit-learn-0.21.0 setuptools-41.6.0 stable-baselines-2.10.1 stockstats-0.3.2 tensorboard-1.15.0 tensorflow-1.15.4 tensorflow-estimator-1.15.1 wheel-0.33.6 yfinance-0.1.55\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits",
                  "pkg_resources"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iXyHD6ir5sxk"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jeFTP-XzTnSV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4accc963-9f26-483d-fb48-361ee26964b0"
      },
      "source": [
        "import pkg_resources\n",
        "import pip\n",
        "installedPackages = {pkg.key for pkg in pkg_resources.working_set}\n",
        "required = {'yfinance', 'pandas', 'matplotlib', 'stockstats','stable-baselines','gym','tensorflow'}\n",
        "missing = required - installedPackages\n",
        "if missing:\n",
        "    !pip install yfinance\n",
        "    !pip install pandas\n",
        "    !pip install matplotlib\n",
        "    !pip install stockstats\n",
        "    !pip install gym\n",
        "    !pip install stable-baselines[mpi]\n",
        "    !pip install tensorflow==1.15.4\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: yfinance in /usr/local/lib/python3.6/dist-packages (0.1.55)\n",
            "Requirement already satisfied: lxml>=4.5.1 in /usr/local/lib/python3.6/dist-packages (from yfinance) (4.6.1)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from yfinance) (2.23.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from yfinance) (0.0.9)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.1.4)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from yfinance) (1.18.5)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2020.11.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance) (3.0.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24->yfinance) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24->yfinance) (1.15.0)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (1.1.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas) (2.8.1)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.6/dist-packages (from pandas) (1.18.5)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (3.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (0.10.0)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.18.5)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (1.3.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.8.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib) (2.4.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib) (1.15.0)\n",
            "Requirement already satisfied: stockstats in /usr/local/lib/python3.6/dist-packages (0.3.2)\n",
            "Requirement already satisfied: int-date>=0.1.7 in /usr/local/lib/python3.6/dist-packages (from stockstats) (0.1.8)\n",
            "Requirement already satisfied: pandas>=0.18.1 in /usr/local/lib/python3.6/dist-packages (from stockstats) (1.1.4)\n",
            "Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.6/dist-packages (from stockstats) (1.18.5)\n",
            "Requirement already satisfied: python-dateutil>=2.4.2 in /usr/local/lib/python3.6/dist-packages (from int-date>=0.1.7->stockstats) (2.8.1)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from int-date>=0.1.7->stockstats) (1.15.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.18.1->stockstats) (2018.9)\n",
            "Requirement already satisfied: gym in /usr/local/lib/python3.6/dist-packages (0.15.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gym) (1.15.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from gym) (1.4.1)\n",
            "Requirement already satisfied: pyglet<=1.3.2,>=1.2.0 in /usr/local/lib/python3.6/dist-packages (from gym) (1.3.2)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.6/dist-packages (from gym) (1.18.5)\n",
            "Requirement already satisfied: cloudpickle~=1.2.0 in /usr/local/lib/python3.6/dist-packages (from gym) (1.2.2)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyglet<=1.3.2,>=1.2.0->gym) (0.16.0)\n",
            "Requirement already satisfied: stable-baselines[mpi] in /usr/local/lib/python3.6/dist-packages (2.10.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from stable-baselines[mpi]) (1.1.4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from stable-baselines[mpi]) (1.18.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from stable-baselines[mpi]) (1.4.1)\n",
            "Requirement already satisfied: gym[atari,classic_control]>=0.11 in /usr/local/lib/python3.6/dist-packages (from stable-baselines[mpi]) (0.15.3)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.6/dist-packages (from stable-baselines[mpi]) (4.1.2.30)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from stable-baselines[mpi]) (0.15.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from stable-baselines[mpi]) (3.2.1)\n",
            "Requirement already satisfied: cloudpickle>=0.5.5 in /usr/local/lib/python3.6/dist-packages (from stable-baselines[mpi]) (1.2.2)\n",
            "Requirement already satisfied: mpi4py; extra == \"mpi\" in /usr/local/lib/python3.6/dist-packages (from stable-baselines[mpi]) (3.0.3)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->stable-baselines[mpi]) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->stable-baselines[mpi]) (2018.9)\n",
            "Requirement already satisfied: pyglet<=1.3.2,>=1.2.0 in /usr/local/lib/python3.6/dist-packages (from gym[atari,classic_control]>=0.11->stable-baselines[mpi]) (1.3.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gym[atari,classic_control]>=0.11->stable-baselines[mpi]) (1.15.0)\n",
            "Requirement already satisfied: Pillow; extra == \"atari\" in /usr/local/lib/python3.6/dist-packages (from gym[atari,classic_control]>=0.11->stable-baselines[mpi]) (7.0.0)\n",
            "Requirement already satisfied: atari-py~=0.2.0; extra == \"atari\" in /usr/local/lib/python3.6/dist-packages (from gym[atari,classic_control]>=0.11->stable-baselines[mpi]) (0.2.6)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->stable-baselines[mpi]) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->stable-baselines[mpi]) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->stable-baselines[mpi]) (0.10.0)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyglet<=1.3.2,>=1.2.0->gym[atari,classic_control]>=0.11->stable-baselines[mpi]) (0.16.0)\n",
            "Requirement already satisfied: tensorflow==1.15.4 in /usr/local/lib/python3.6/dist-packages (1.15.4)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (1.12.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (1.1.2)\n",
            "Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (0.2.2)\n",
            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (0.33.6)\n",
            "Requirement already satisfied: tensorflow-estimator==1.15.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (1.15.1)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (1.0.8)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (1.15.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (0.8.1)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (1.33.2)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (0.10.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (3.3.0)\n",
            "Requirement already satisfied: tensorboard<1.16.0,>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (1.15.0)\n",
            "Requirement already satisfied: numpy<1.19.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (1.18.5)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (1.1.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (3.12.4)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4) (0.2.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==1.15.4) (2.10.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.4) (3.3.3)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.4) (41.6.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.4) (1.0.1)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.4) (2.0.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.4) (3.4.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QG17M4JwTnSZ"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-0bsNMMTnSZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45e749f8-519d-4c88-bd80-a115fcb22f8c"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "from finrl.config import config\n",
        "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
        "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
        "from finrl.preprocessing.data import data_split\n",
        "from finrl.env.environment import EnvSetup\n",
        "from finrl.env.EnvMultipleStock_train import StockEnvTrain\n",
        "from finrl.env.EnvMultipleStock_trade import StockEnvTrade\n",
        "from finrl.model.models import DRLAgent\n",
        "from finrl.trade.backtest import BackTestStats, BaselineStats, BackTestPlot\n",
        "\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
            "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIPbzYs1TnSd"
      },
      "source": [
        "#Diable the warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pos2IZAL54pp"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zp6lL6dZ53rX"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SBPM0sVvTnSg"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GCGVmtGzjORf"
      },
      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    Provides methods for retrieving daily stock data from\n",
        "    Yahoo Finance API\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        start_date : str\n",
        "            start date of the data (modified from config.py)\n",
        "        end_date : str\n",
        "            end date of the data (modified from config.py)\n",
        "        ticker_list : list\n",
        "            a list of stock tickers (modified from config.py)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    fetch_data()\n",
        "        Fetches data from yahoo API\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FBEiH5gOgMOx",
        "outputId": "4bc37537-03de-4ac1-c8d8-dd621fc94b0d"
      },
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2009-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "sWLMNQ8CgMRx",
        "outputId": "a56bb669-bd25-4199-8d48-3ff905e3120a"
      },
      "source": [
        "# from config.py end_date is a string\n",
        "config.END_DATE"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2020-09-30'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AmFpuBEZhkF3"
      },
      "source": [
        "ticker_list is a list of stock tickers, in a single stock trading case, the list contains only 1 ticker"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JtIFikNyTnSg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d333abe-d121-4b6e-badb-1208e72c8aa0"
      },
      "source": [
        "# Download and save the data in a pandas DataFrame:\n",
        "data_df = YahooDownloader(start_date = config.START_DATE,\n",
        "                          end_date = config.END_DATE,\n",
        "                          ticker_list = ['AAPL']).fetch_data()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (2956, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8ZKQmw6TnSl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd3763cb-bbbd-4cfa-963a-608eee903334"
      },
      "source": [
        "data_df.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2956, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vi6D_ED6TnSs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "c06501e0-e0d1-4fc7-bc41-7107e7327ed1"
      },
      "source": [
        "data_df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.773207</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-05</td>\n",
              "      <td>3.327500</td>\n",
              "      <td>3.435000</td>\n",
              "      <td>3.311071</td>\n",
              "      <td>2.890248</td>\n",
              "      <td>1181608400</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-06</td>\n",
              "      <td>3.426786</td>\n",
              "      <td>3.470357</td>\n",
              "      <td>3.299643</td>\n",
              "      <td>2.842576</td>\n",
              "      <td>1289310400</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-07</td>\n",
              "      <td>3.278929</td>\n",
              "      <td>3.303571</td>\n",
              "      <td>3.223572</td>\n",
              "      <td>2.781153</td>\n",
              "      <td>753048800</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-08</td>\n",
              "      <td>3.229643</td>\n",
              "      <td>3.326786</td>\n",
              "      <td>3.215714</td>\n",
              "      <td>2.832797</td>\n",
              "      <td>673500800</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date      open      high       low     close      volume   tic\n",
              "0  2009-01-02  3.067143  3.251429  3.041429  2.773207   746015200  AAPL\n",
              "1  2009-01-05  3.327500  3.435000  3.311071  2.890248  1181608400  AAPL\n",
              "2  2009-01-06  3.426786  3.470357  3.299643  2.842576  1289310400  AAPL\n",
              "3  2009-01-07  3.278929  3.303571  3.223572  2.781153   753048800  AAPL\n",
              "4  2009-01-08  3.229643  3.326786  3.215714  2.832797   673500800  AAPL"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWiqgpLzTnS3"
      },
      "source": [
        "<a id='3'></a>\n",
        "# Part 4. Preprocess Data\n",
        "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
        "* FinRL uses a class **FeatureEngineer** to preprocess the data\n",
        "* Add **technical indicators**. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJ9zmxpRks41"
      },
      "source": [
        "class FeatureEngineer:\n",
        "Provides methods for preprocessing the stock price data\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        df: DataFrame\n",
        "            data downloaded from Yahoo API\n",
        "        feature_number : int\n",
        "            number of features we used\n",
        "        use_technical_indicator : boolean\n",
        "            we technical indicator or not\n",
        "        use_turbulence : boolean\n",
        "            use turbulence index or not\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    preprocess_data()\n",
        "        main method to do the feature engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHu7i-T_wRPc"
      },
      "source": [
        "<a id='3.1'></a>\n",
        "\n",
        "## 4.1 Technical Indicators\n",
        "* FinRL uses stockstats to calcualte technical indicators such as **Moving Average Convergence Divergence (MACD)**, **Relative Strength Index (RSI)**, **Average Directional Index (ADX)**, **Commodity Channel Index (CCI)** and other various indicators and stats.\n",
        "* **stockstats**: supplies a wrapper StockDataFrame based on the **pandas.DataFrame** with inline stock statistics/indicators support.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2RHwY1dHk09N",
        "outputId": "33cdb93e-6cf0-4e7e-d840-e4eef395b1a7"
      },
      "source": [
        "## we store the stockstats technical indicator column names in config.py\n",
        "tech_indicator_list=config.TECHNICAL_INDICATORS_LIST\n",
        "print(tech_indicator_list)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['macd', 'rsi_30', 'cci_30', 'dx_30']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rImfGAfCkR8j",
        "outputId": "c7608732-c191-4a10-afd4-c6842de2588d"
      },
      "source": [
        "## user can add more technical indicators\n",
        "## check https://github.com/jealous/stockstats for different names\n",
        "tech_indicator_list=tech_indicator_list+['kdjk','open_2_sma','boll','close_10.0_le_5_c','wr_10','dma','trix']\n",
        "print(tech_indicator_list)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['macd', 'rsi_30', 'cci_30', 'dx_30', 'kdjk', 'open_2_sma', 'boll', 'close_10.0_le_5_c', 'wr_10', 'dma', 'trix']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etvRo2rSwZPg"
      },
      "source": [
        "<a id='3.2'></a>\n",
        "## 4.2 Perform Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7EQlXSl1TnS4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b736c72f-ac66-4767-9ade-849e31af33e8"
      },
      "source": [
        "data_df = FeatureEngineer(data_df.copy(),\n",
        "                          use_technical_indicator=True,\n",
        "                          tech_indicator_list = tech_indicator_list,\n",
        "                          use_turbulence=False,\n",
        "                          user_defined_feature = True).preprocess_data()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n",
            "Successfully added user defined features\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "id": "wytX_qwWMHP5",
        "outputId": "5c9914b3-da0d-41af-f987-42bdcd96d401"
      },
      "source": [
        "data_df.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>kdjk</th>\n",
              "      <th>open_2_sma</th>\n",
              "      <th>boll</th>\n",
              "      <th>close_10.0_le_5_c</th>\n",
              "      <th>wr_10</th>\n",
              "      <th>dma</th>\n",
              "      <th>trix</th>\n",
              "      <th>daily_return</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.773207</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>-9.241443</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>2.773207</td>\n",
              "      <td>1.0</td>\n",
              "      <td>227.724329</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.670733</td>\n",
              "      <td>0.042204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-05</td>\n",
              "      <td>3.327500</td>\n",
              "      <td>3.435000</td>\n",
              "      <td>3.311071</td>\n",
              "      <td>2.890248</td>\n",
              "      <td>1181608400</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.002626</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>-18.965140</td>\n",
              "      <td>3.197322</td>\n",
              "      <td>2.831728</td>\n",
              "      <td>2.0</td>\n",
              "      <td>138.412535</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.670733</td>\n",
              "      <td>0.042204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-06</td>\n",
              "      <td>3.426786</td>\n",
              "      <td>3.470357</td>\n",
              "      <td>3.299643</td>\n",
              "      <td>2.842576</td>\n",
              "      <td>1289310400</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.001868</td>\n",
              "      <td>70.355256</td>\n",
              "      <td>46.810872</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>-28.096875</td>\n",
              "      <td>3.377143</td>\n",
              "      <td>2.835344</td>\n",
              "      <td>3.0</td>\n",
              "      <td>146.360343</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.391302</td>\n",
              "      <td>-0.016494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-07</td>\n",
              "      <td>3.278929</td>\n",
              "      <td>3.303571</td>\n",
              "      <td>3.223572</td>\n",
              "      <td>2.781153</td>\n",
              "      <td>753048800</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.000741</td>\n",
              "      <td>50.429273</td>\n",
              "      <td>-29.735567</td>\n",
              "      <td>43.608349</td>\n",
              "      <td>-38.958050</td>\n",
              "      <td>3.352857</td>\n",
              "      <td>2.821796</td>\n",
              "      <td>4.0</td>\n",
              "      <td>160.680401</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.195391</td>\n",
              "      <td>-0.021608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-08</td>\n",
              "      <td>3.229643</td>\n",
              "      <td>3.326786</td>\n",
              "      <td>3.215714</td>\n",
              "      <td>2.832797</td>\n",
              "      <td>673500800</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.000087</td>\n",
              "      <td>60.227005</td>\n",
              "      <td>-9.052599</td>\n",
              "      <td>48.358256</td>\n",
              "      <td>-42.185416</td>\n",
              "      <td>3.254286</td>\n",
              "      <td>2.823996</td>\n",
              "      <td>5.0</td>\n",
              "      <td>148.640149</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.125123</td>\n",
              "      <td>0.018569</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date      open      high  ...  dma      trix  daily_return\n",
              "0  2009-01-02  3.067143  3.251429  ...  0.0  0.670733      0.042204\n",
              "1  2009-01-05  3.327500  3.435000  ...  0.0  0.670733      0.042204\n",
              "2  2009-01-06  3.426786  3.470357  ...  0.0  0.391302     -0.016494\n",
              "3  2009-01-07  3.278929  3.303571  ...  0.0  0.195391     -0.021608\n",
              "4  2009-01-08  3.229643  3.326786  ...  0.0  0.125123      0.018569\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwLhXo1cTnTQ"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Build Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1D1FlBdOL4b3"
      },
      "source": [
        "<a id='4.1'></a>\n",
        "## 5.1 Training & Trade data split\n",
        "* Training: 2009-01-01 to 2018-12-31\n",
        "* Trade: 2019-01-01 to 2020-09-30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNOdqfTKL6K-"
      },
      "source": [
        "train = data_split(data_df, start = config.START_DATE, end = config.START_TRADE_DATE)\n",
        "trade = data_split(data_df, start = config.START_TRADE_DATE, end = config.END_DATE)\n",
        "#train = data_split(data_df, start = '2009-01-01', end = '2019-01-01')\n",
        "#trade = data_split(data_df, start = '2019-01-01', end = '2020-09-30')\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KUxOshVouLXt",
        "outputId": "1ab5b24e-db21-404f-e35d-f8083afcabdd"
      },
      "source": [
        "## data normalization, this part is optional, have little impact\n",
        "feaures_list = list(train.columns)\n",
        "feaures_list.remove('date')\n",
        "feaures_list.remove('tic')\n",
        "feaures_list.remove('close')\n",
        "print(feaures_list)\n",
        "from sklearn import preprocessing\n",
        "data_normaliser = preprocessing.StandardScaler()\n",
        "train[feaures_list] = data_normaliser.fit_transform(train[feaures_list])\n",
        "trade[feaures_list] = data_normaliser.transform(trade[feaures_list])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['open', 'high', 'low', 'volume', 'macd', 'rsi_30', 'cci_30', 'dx_30', 'kdjk', 'open_2_sma', 'boll', 'close_10.0_le_5_c', 'wr_10', 'dma', 'trix', 'daily_return']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KMHyaSBBDGbe"
      },
      "source": [
        "<a id='4.2'></a>\n",
        "## 5.2 User-defined Environment: a simulation environment class "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5qqW-mnSTnTQ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from gym.utils import seeding\n",
        "import gym\n",
        "from gym import spaces\n",
        "import matplotlib\n",
        "matplotlib.use('Agg')\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "class SingleStockEnv(gym.Env):\n",
        "    \"\"\"A single stock trading environment for OpenAI gym\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        df: DataFrame\n",
        "            input data\n",
        "        stock_dim : int\n",
        "            number of unique stocks\n",
        "        hmax : int\n",
        "            maximum number of shares to trade\n",
        "        initial_amount : int\n",
        "            start money\n",
        "        transaction_cost_pct: float\n",
        "            transaction cost percentage per trade\n",
        "        reward_scaling: float\n",
        "            scaling factor for reward, good for training\n",
        "        state_space: int\n",
        "            the dimension of input features\n",
        "        action_space: int\n",
        "            equals stock dimension\n",
        "        tech_indicator_list: list\n",
        "            a list of technical indicator names\n",
        "        turbulence_threshold: int\n",
        "            a threshold to control risk aversion\n",
        "        day: int\n",
        "            an increment number to control date\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    _sell_stock()\n",
        "        perform sell action based on the sign of the action\n",
        "    _buy_stock()\n",
        "        perform buy action based on the sign of the action\n",
        "    step()\n",
        "        at each step the agent will return actions, then \n",
        "        we will calculate the reward, and return the next observation.\n",
        "    reset()\n",
        "        reset the environment\n",
        "    render()\n",
        "        use render to return other functions\n",
        "    save_asset_memory()\n",
        "        return account value at each time step\n",
        "    save_action_memory()\n",
        "        return actions/positions at each time step\n",
        "        \n",
        "\n",
        "    \"\"\"\n",
        "    metadata = {'render.modes': ['human']}\n",
        "\n",
        "    def __init__(self, \n",
        "                df,\n",
        "                stock_dim,\n",
        "                hmax,\n",
        "                initial_amount,\n",
        "                transaction_cost_pct,\n",
        "                reward_scaling,\n",
        "                state_space,\n",
        "                action_space,\n",
        "                tech_indicator_list,\n",
        "                turbulence_threshold,\n",
        "                day = 0):\n",
        "        #super(StockEnv, self).__init__()\n",
        "        #money = 10 , scope = 1\n",
        "        self.day = day\n",
        "        self.df = df\n",
        "        self.stock_dim = stock_dim\n",
        "        self.hmax = hmax\n",
        "        self.initial_amount = initial_amount\n",
        "        self.transaction_cost_pct =transaction_cost_pct\n",
        "        self.reward_scaling = reward_scaling\n",
        "        self.state_space = state_space\n",
        "        self.action_space = action_space\n",
        "        self.tech_indicator_list = tech_indicator_list\n",
        "\n",
        "        # action_space normalization and shape is self.stock_dim\n",
        "        self.action_space = spaces.Box(low = -1, high = 1,shape = (self.action_space,)) \n",
        "        # Shape = 181: [Current Balance]+[prices 1-30]+[owned shares 1-30] \n",
        "        # +[macd 1-30]+ [rsi 1-30] + [cci 1-30] + [adx 1-30]\n",
        "        self.observation_space = spaces.Box(low=0, high=np.inf, shape = (self.state_space,))\n",
        "        # load data from a pandas dataframe\n",
        "        self.data = self.df.loc[self.day,:]\n",
        "        self.terminal = False     \n",
        "        self.turbulence_threshold = turbulence_threshold        \n",
        "        # initalize state: inital amount + close price + shares + technical indicators + other features\n",
        "        self.state = [self.initial_amount] + \\\n",
        "                      [self.data.close] + \\\n",
        "                      [0]*self.stock_dim  + \\\n",
        "                      sum([[self.data[tech]] for tech in self.tech_indicator_list ], [])+ \\\n",
        "                      [self.data.open] + \\\n",
        "                      [self.data.high] + \\\n",
        "                      [self.data.low] +\\\n",
        "                      [self.data.daily_return] \n",
        "        # initialize reward\n",
        "        self.reward = 0\n",
        "        self.cost = 0\n",
        "        # memorize all the total balance change\n",
        "        self.asset_memory = [self.initial_amount]\n",
        "        self.rewards_memory = []\n",
        "        self.actions_memory=[]\n",
        "        self.date_memory=[self.data.date]\n",
        "        self.close_price_memory = [self.data.close]\n",
        "        self.trades = 0\n",
        "        self._seed()\n",
        "\n",
        "\n",
        "    def _sell_stock(self, index, action):\n",
        "        # perform sell action based on the sign of the action\n",
        "        if self.state[index+self.stock_dim+1] > 0:\n",
        "            #update balance\n",
        "            self.state[0] += \\\n",
        "            self.state[index+1]*min(abs(action),self.state[index+self.stock_dim+1]) * \\\n",
        "             (1- self.transaction_cost_pct)\n",
        "\n",
        "            self.state[index+self.stock_dim+1] -= min(abs(action), self.state[index+self.stock_dim+1])\n",
        "            self.cost +=self.state[index+1]*min(abs(action),self.state[index+self.stock_dim+1]) * \\\n",
        "             self.transaction_cost_pct\n",
        "            self.trades+=1\n",
        "        else:\n",
        "            pass\n",
        "\n",
        "    \n",
        "    def _buy_stock(self, index, action):\n",
        "        # perform buy action based on the sign of the action\n",
        "        available_amount = self.state[0] // self.state[index+1]\n",
        "        # print('available_amount:{}'.format(available_amount))\n",
        "\n",
        "        #update balance\n",
        "        self.state[0] -= self.state[index+1]*min(available_amount, action)* \\\n",
        "                          (1+ self.transaction_cost_pct)\n",
        "\n",
        "        self.state[index+self.stock_dim+1] += min(available_amount, action)\n",
        "\n",
        "        self.cost+=self.state[index+1]*min(available_amount, action)* \\\n",
        "                          self.transaction_cost_pct\n",
        "        self.trades+=1\n",
        "        \n",
        "    def step(self, actions):\n",
        "        # print(self.day)\n",
        "        self.terminal = self.day >= len(self.df.index.unique())-1\n",
        "        # print(actions)\n",
        "\n",
        "        if self.terminal:\n",
        "            #plt.plot(self.asset_memory,'r')\n",
        "            #plt.savefig('results/account_value_train.png')\n",
        "            #plt.close()\n",
        "            end_total_asset = self.state[0]+ \\\n",
        "                sum(np.array(self.state[1:(self.stock_dim+1)])*np.array(self.state[(self.stock_dim+1):(self.stock_dim*2+1)]))\n",
        "\n",
        "            print(\"begin_total_asset:{}\".format(self.asset_memory[0]))           \n",
        "            print(\"end_total_asset:{}\".format(end_total_asset))\n",
        "            df_total_value = pd.DataFrame(self.asset_memory)\n",
        "            #df_total_value.to_csv('results/account_value_train.csv')\n",
        "            print(\"total_reward:{}\".format(self.state[0]+sum(np.array(self.state[1:(self.stock_dim+1)])*np.array(self.state[(self.stock_dim+1):(self.stock_dim*2+1)]))- self.initial_amount ))\n",
        "            print(\"total_cost: \", self.cost)\n",
        "            print(\"total_trades: \", self.trades)\n",
        "            df_total_value.columns = ['account_value']\n",
        "            df_total_value['daily_return']=df_total_value.pct_change(1)\n",
        "            if df_total_value['daily_return'].std() !=0:\n",
        "              sharpe = (252**0.5)*df_total_value['daily_return'].mean()/ \\\n",
        "                    df_total_value['daily_return'].std()\n",
        "              print(\"Sharpe: \",sharpe)\n",
        "              print(\"=================================\")\n",
        "            df_rewards = pd.DataFrame(self.rewards_memory)\n",
        "            #df_rewards.to_csv('results/account_rewards_train.csv')\n",
        "            \n",
        "            \n",
        "            return self.state, self.reward, self.terminal,{}\n",
        "\n",
        "        else:\n",
        "            #print(actions)\n",
        "            actions = actions * self.hmax\n",
        "            self.actions_memory.append(actions)\n",
        "            #actions = (actions.astype(int))\n",
        "            \n",
        "            begin_total_asset = self.state[0]+ \\\n",
        "            sum(np.array(self.state[1:(self.stock_dim+1)])*np.array(self.state[(self.stock_dim+1):(self.stock_dim*2+1)]))\n",
        "            #print(\"begin_total_asset:{}\".format(begin_total_asset))\n",
        "            \n",
        "            argsort_actions = np.argsort(actions)\n",
        "            \n",
        "            sell_index = argsort_actions[:np.where(actions < 0)[0].shape[0]]\n",
        "            buy_index = argsort_actions[::-1][:np.where(actions > 0)[0].shape[0]]\n",
        "\n",
        "            for index in sell_index:\n",
        "                # print('take sell action'.format(actions[index]))\n",
        "                self._sell_stock(index, actions[index])\n",
        "\n",
        "            for index in buy_index:\n",
        "                # print('take buy action: {}'.format(actions[index]))\n",
        "                self._buy_stock(index, actions[index])\n",
        "\n",
        "            self.day += 1\n",
        "            self.data = self.df.loc[self.day,:]         \n",
        "            #load next state\n",
        "            # print(\"stock_shares:{}\".format(self.state[29:]))\n",
        "            self.state =  [self.state[0]] + \\\n",
        "                    [self.data.close] + \\\n",
        "                    list(self.state[(self.stock_dim+1):(self.stock_dim*2+1)]) + \\\n",
        "                      sum([[self.data[tech]] for tech in self.tech_indicator_list ], [])+ \\\n",
        "                      [self.data.open] + \\\n",
        "                      [self.data.high] + \\\n",
        "                      [self.data.low] +\\\n",
        "                      [self.data.daily_return] \n",
        "            \n",
        "            end_total_asset = self.state[0]+ \\\n",
        "            sum(np.array(self.state[1:(self.stock_dim+1)])*np.array(self.state[(self.stock_dim+1):(self.stock_dim*2+1)]))\n",
        "            self.asset_memory.append(end_total_asset)\n",
        "            self.date_memory.append(self.data.date)\n",
        "            self.close_price_memory.append(self.data.close)\n",
        "\n",
        "            #print(\"end_total_asset:{}\".format(end_total_asset))\n",
        "            \n",
        "            self.reward = end_total_asset - begin_total_asset            \n",
        "            # print(\"step_reward:{}\".format(self.reward))\n",
        "            self.rewards_memory.append(self.reward)\n",
        "            \n",
        "            self.reward = self.reward*self.reward_scaling\n",
        "\n",
        "\n",
        "\n",
        "        return self.state, self.reward, self.terminal, {}\n",
        "\n",
        "    def reset(self):\n",
        "        self.asset_memory = [self.initial_amount]\n",
        "        self.day = 0\n",
        "        self.data = self.df.loc[self.day,:]\n",
        "        self.cost = 0\n",
        "        self.trades = 0\n",
        "        self.terminal = False \n",
        "        self.rewards_memory = []\n",
        "        self.actions_memory=[]\n",
        "        self.date_memory=[self.data.date]\n",
        "        #initiate state\n",
        "        self.state = [self.initial_amount] + \\\n",
        "                      [self.data.close] + \\\n",
        "                      [0]*self.stock_dim + \\\n",
        "                      sum([[self.data[tech]] for tech in self.tech_indicator_list ], [])+ \\\n",
        "                      [self.data.open] + \\\n",
        "                      [self.data.high] + \\\n",
        "                      [self.data.low] +\\\n",
        "                      [self.data.daily_return] \n",
        "        return self.state\n",
        "    \n",
        "    def render(self, mode='human'):\n",
        "        return self.state\n",
        "    \n",
        "    def save_asset_memory(self):\n",
        "        date_list = self.date_memory\n",
        "        asset_list = self.asset_memory\n",
        "        #print(len(date_list))\n",
        "        #print(len(asset_list))\n",
        "        df_account_value = pd.DataFrame({'date':date_list,'account_value':asset_list})\n",
        "        return df_account_value\n",
        "\n",
        "    def save_action_memory(self):\n",
        "        # date and close price length must match actions length\n",
        "        date_list = self.date_memory[:-1]\n",
        "        close_price_list = self.close_price_memory[:-1]\n",
        "\n",
        "        action_list = self.actions_memory\n",
        "        df_actions = pd.DataFrame({'date':date_list,'actions':action_list,'close_price':close_price_list})\n",
        "        return df_actions\n",
        "\n",
        "    def _seed(self, seed=None):\n",
        "        self.np_random, seed = seeding.np_random(seed)\n",
        "        return [seed]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90V3S7cpDcQs"
      },
      "source": [
        "<a id='4.3'></a>\n",
        "## 5.3 Initialize Environment\n",
        "* **stock dimension**: the number of unique stock tickers we use\n",
        "* **hmax**: the maximum amount of shares to buy or sell\n",
        "* **initial amount**: the amount of money we use to trade in the begining\n",
        "* **transaction cost percentage**: a per share rate for every share trade\n",
        "* **tech_indicator_list**: a list of technical indicator names (modified from config.py)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OiH0xO96mGcL",
        "outputId": "bc939350-b47e-4838-ebcc-1e21313c4a63"
      },
      "source": [
        "## we store the stockstats technical indicator column names in config.py\n",
        "## check https://github.com/jealous/stockstats for different names\n",
        "tech_indicator_list"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['macd',\n",
              " 'rsi_30',\n",
              " 'cci_30',\n",
              " 'dx_30',\n",
              " 'kdjk',\n",
              " 'open_2_sma',\n",
              " 'boll',\n",
              " 'close_10.0_le_5_c',\n",
              " 'wr_10',\n",
              " 'dma',\n",
              " 'trix']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnWOKS7DyGM7",
        "outputId": "737f9569-c68e-42cd-ae45-451e00e3ef11"
      },
      "source": [
        "# the stock dimension is 1, because we only use the price data of AAPL.\n",
        "len(train.tic.unique())"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ipJcnLvQGAba",
        "outputId": "b8d8a686-47ae-4d56-be0e-d3c394726ae5"
      },
      "source": [
        "# account balance + close price + shares + technical indicators + open-high-low-price + 1 returns\n",
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(tech_indicator_list)*stock_dimension + 4*stock_dimension\n",
        "print(state_space)\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "18\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jIGqfq0FTnTT"
      },
      "source": [
        "env_setup = EnvSetup(stock_dim = stock_dimension,\n",
        "                     state_space = state_space,\n",
        "                     hmax = 200,\n",
        "                     initial_amount = 100000,\n",
        "                     transaction_cost_pct = 0.001,\n",
        "                     tech_indicator_list = tech_indicator_list)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmyi2IPnyEkP"
      },
      "source": [
        "env_train = env_setup.create_env_training(data = train,\n",
        "                                          env_class = SingleStockEnv)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "id": "q0Vf3NIcF1bE",
        "outputId": "0bc5be6d-89e7-4c3c-d7a6-5ed84143ed92"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>kdjk</th>\n",
              "      <th>open_2_sma</th>\n",
              "      <th>boll</th>\n",
              "      <th>close_10.0_le_5_c</th>\n",
              "      <th>wr_10</th>\n",
              "      <th>dma</th>\n",
              "      <th>trix</th>\n",
              "      <th>daily_return</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>-1.578215</td>\n",
              "      <td>-1.566845</td>\n",
              "      <td>-1.576249</td>\n",
              "      <td>2.773207</td>\n",
              "      <td>1.618116</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.241386</td>\n",
              "      <td>4.683665</td>\n",
              "      <td>0.312711</td>\n",
              "      <td>4.127094</td>\n",
              "      <td>1.206457</td>\n",
              "      <td>-1.577762</td>\n",
              "      <td>-1.464083</td>\n",
              "      <td>-0.010711</td>\n",
              "      <td>0.357995</td>\n",
              "      <td>-0.244284</td>\n",
              "      <td>1.906911</td>\n",
              "      <td>2.445709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-05</td>\n",
              "      <td>-1.557496</td>\n",
              "      <td>-1.552355</td>\n",
              "      <td>-1.554610</td>\n",
              "      <td>2.890248</td>\n",
              "      <td>3.377073</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.235643</td>\n",
              "      <td>4.683665</td>\n",
              "      <td>0.312711</td>\n",
              "      <td>4.127094</td>\n",
              "      <td>1.086825</td>\n",
              "      <td>-1.567402</td>\n",
              "      <td>-1.459330</td>\n",
              "      <td>0.488327</td>\n",
              "      <td>-0.678000</td>\n",
              "      <td>-0.244284</td>\n",
              "      <td>1.906911</td>\n",
              "      <td>2.445709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-06</td>\n",
              "      <td>-1.549595</td>\n",
              "      <td>-1.549564</td>\n",
              "      <td>-1.555527</td>\n",
              "      <td>2.842576</td>\n",
              "      <td>3.811981</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.237302</td>\n",
              "      <td>1.552373</td>\n",
              "      <td>0.131576</td>\n",
              "      <td>4.127094</td>\n",
              "      <td>0.974475</td>\n",
              "      <td>-1.553091</td>\n",
              "      <td>-1.459036</td>\n",
              "      <td>0.987365</td>\n",
              "      <td>-0.585807</td>\n",
              "      <td>-0.244284</td>\n",
              "      <td>0.958661</td>\n",
              "      <td>-1.055377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-07</td>\n",
              "      <td>-1.561361</td>\n",
              "      <td>-1.562729</td>\n",
              "      <td>-1.561632</td>\n",
              "      <td>2.781153</td>\n",
              "      <td>1.646519</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.243006</td>\n",
              "      <td>-0.552353</td>\n",
              "      <td>-0.566723</td>\n",
              "      <td>1.011709</td>\n",
              "      <td>0.840848</td>\n",
              "      <td>-1.555024</td>\n",
              "      <td>-1.460137</td>\n",
              "      <td>1.486403</td>\n",
              "      <td>-0.419698</td>\n",
              "      <td>-0.244284</td>\n",
              "      <td>0.293837</td>\n",
              "      <td>-1.360408</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-08</td>\n",
              "      <td>-1.565283</td>\n",
              "      <td>-1.560897</td>\n",
              "      <td>-1.562262</td>\n",
              "      <td>2.832797</td>\n",
              "      <td>1.325298</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-0.241577</td>\n",
              "      <td>0.482554</td>\n",
              "      <td>-0.378042</td>\n",
              "      <td>1.274120</td>\n",
              "      <td>0.801141</td>\n",
              "      <td>-1.562869</td>\n",
              "      <td>-1.459958</td>\n",
              "      <td>1.985441</td>\n",
              "      <td>-0.559362</td>\n",
              "      <td>-0.244284</td>\n",
              "      <td>0.055384</td>\n",
              "      <td>1.036004</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date      open      high  ...       dma      trix  daily_return\n",
              "0  2009-01-02 -1.578215 -1.566845  ... -0.244284  1.906911      2.445709\n",
              "1  2009-01-05 -1.557496 -1.552355  ... -0.244284  1.906911      2.445709\n",
              "2  2009-01-06 -1.549595 -1.549564  ... -0.244284  0.958661     -1.055377\n",
              "3  2009-01-07 -1.561361 -1.562729  ... -0.244284  0.293837     -1.360408\n",
              "4  2009-01-08 -1.565283 -1.560897  ... -0.244284  0.055384      1.036004\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mdnzYtM1TnTW"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Implement DRL Algorithms\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BRFIZDw8TnTX"
      },
      "source": [
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zD1NHzGyTnTc"
      },
      "source": [
        "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y9CM5DeIr9GC"
      },
      "source": [
        "### Model 1: A2C"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTMEM4Or02Rj",
        "outputId": "8222d50e-1362-4938-8b39-064936b79d1d"
      },
      "source": [
        "## default hyperparameters in config file\n",
        "config.A2C_PARAMS"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ent_coef': 0.01,\n",
              " 'learning_rate': 0.0007,\n",
              " 'n_steps': 5,\n",
              " 'timesteps': 20000,\n",
              " 'verbose': 0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "UJs0N4dGTnTd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c077814c-bde5-4b70-bd21-f6c167d726a2"
      },
      "source": [
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "a2c_params_tuning = {'n_steps':5, \n",
        "\t\t\t  'ent_coef':0.005, \n",
        "\t\t\t  'learning_rate':0.0007,\n",
        "\t\t\t  'verbose':0,\n",
        "\t\t\t  'timesteps':100000}\n",
        "model_a2c = agent.train_A2C(model_name = \"A2C_{}\".format(now), model_params = a2c_params_tuning)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "begin_total_asset:100000\n",
            "end_total_asset:176934.7576968735\n",
            "total_reward:76934.75769687351\n",
            "total_cost:  5882.835153967686\n",
            "total_trades:  2484\n",
            "Sharpe:  0.46981434691347806\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:595867.5745766863\n",
            "total_reward:495867.57457668625\n",
            "total_cost:  4290.078180151586\n",
            "total_trades:  2514\n",
            "Sharpe:  0.8764031127847676\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:583671.8077524664\n",
            "total_reward:483671.8077524664\n",
            "total_cost:  5838.791503323599\n",
            "total_trades:  2512\n",
            "Sharpe:  0.8828870827729837\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:637429.0815745457\n",
            "total_reward:537429.0815745457\n",
            "total_cost:  3895.962820358061\n",
            "total_trades:  2514\n",
            "Sharpe:  0.8993083850920852\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:766699.1715777694\n",
            "total_reward:666699.1715777694\n",
            "total_cost:  1336.049787657923\n",
            "total_trades:  2515\n",
            "Sharpe:  0.9528759647152936\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:882677.1870489779\n",
            "total_reward:782677.1870489779\n",
            "total_cost:  785.3824416674332\n",
            "total_trades:  2515\n",
            "Sharpe:  1.000739173295064\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:927423.8880478856\n",
            "total_reward:827423.8880478856\n",
            "total_cost:  254.9934727955073\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0182559497960086\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1003931.2516248588\n",
            "total_reward:903931.2516248588\n",
            "total_cost:  103.18390643660977\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0458180695295847\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1034917.0496185564\n",
            "total_reward:934917.0496185564\n",
            "total_cost:  115.83752941795201\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0560390646001476\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1028252.0867060601\n",
            "total_reward:928252.0867060601\n",
            "total_cost:  504.6352044569054\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0539729580189\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1012919.8832652074\n",
            "total_reward:912919.8832652074\n",
            "total_cost:  1087.4567894795407\n",
            "total_trades:  2515\n",
            "Sharpe:  1.049357005305756\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1009170.4684736083\n",
            "total_reward:909170.4684736083\n",
            "total_cost:  330.15603324727704\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0477097038943333\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1008728.6930000533\n",
            "total_reward:908728.6930000533\n",
            "total_cost:  105.3839081911078\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0473237020347772\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1066405.9457223369\n",
            "total_reward:966405.9457223369\n",
            "total_cost:  99.93001295428193\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0661702887529563\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1076095.1269021085\n",
            "total_reward:976095.1269021085\n",
            "total_cost:  99.8999331866183\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0691763688716032\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1076202.7779122659\n",
            "total_reward:976202.7779122659\n",
            "total_cost:  99.89889871461293\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0692246929592815\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1076713.6513732625\n",
            "total_reward:976713.6513732625\n",
            "total_cost:  99.89764339307739\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0693742840547629\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1073821.6024768997\n",
            "total_reward:973821.6024768997\n",
            "total_cost:  99.89993767998253\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0684508094123852\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1071677.1316402173\n",
            "total_reward:971677.1316402173\n",
            "total_cost:  99.89588509830196\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0678225871360378\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1073289.289490049\n",
            "total_reward:973289.289490049\n",
            "total_cost:  99.89958180492658\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0683035175424689\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1075447.4353602997\n",
            "total_reward:975447.4353602997\n",
            "total_cost:  99.89794734264501\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0689847753454724\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1049675.3015436474\n",
            "total_reward:949675.3015436474\n",
            "total_cost:  100.54921497903216\n",
            "total_trades:  2515\n",
            "Sharpe:  1.060809128824055\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1047590.4577336748\n",
            "total_reward:947590.4577336748\n",
            "total_cost:  101.89706915307313\n",
            "total_trades:  2514\n",
            "Sharpe:  1.0602217979823982\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1047776.4501636802\n",
            "total_reward:947776.4501636802\n",
            "total_cost:  102.07005234048698\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0602399076152227\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1012040.9935228077\n",
            "total_reward:912040.9935228077\n",
            "total_cost:  106.2468007798095\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0486006596980257\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:981578.9094083403\n",
            "total_reward:881578.9094083403\n",
            "total_cost:  110.2152610478695\n",
            "total_trades:  2514\n",
            "Sharpe:  1.0376627607165896\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1011185.4051029399\n",
            "total_reward:911185.4051029399\n",
            "total_cost:  106.32009020648678\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0481405595104392\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:921929.9658751409\n",
            "total_reward:821929.9658751409\n",
            "total_cost:  116.26213817031766\n",
            "total_trades:  2513\n",
            "Sharpe:  1.0158525285971\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:937270.7163539234\n",
            "total_reward:837270.7163539234\n",
            "total_cost:  116.24364382860685\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0219615653157366\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1006007.6539997341\n",
            "total_reward:906007.6539997341\n",
            "total_cost:  106.56197241422906\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0462902027048853\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:988280.4663097259\n",
            "total_reward:888280.4663097259\n",
            "total_cost:  107.06134741607173\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0401128283240537\n",
            "=================================\n",
            "Training time (A2C):  3.6911093990008035  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9upN8FI2r_X1"
      },
      "source": [
        "### Model 2: DDPG"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pb8YIdvFzHe9",
        "outputId": "4349540b-03ac-45c4-da78-dadeb24f5608"
      },
      "source": [
        "## default hyperparameters in config file\n",
        "config.DDPG_PARAMS"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'batch_size': 128, 'buffer_size': 50000, 'timesteps': 20000, 'verbose': 0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "im7iec5_zJVk",
        "outputId": "9b9240e2-3fbe-44d7-f36d-6e2faa3fdcde"
      },
      "source": [
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "ddpg_params_tuning = {\n",
        "                     'batch_size': 128,\n",
        "\t\t\t               'buffer_size':100000, \n",
        "\t\t\t               'verbose':0,\n",
        "\t\t\t               'timesteps':50000}\n",
        "model_ddpg = agent.train_DDPG(model_name = \"DDPG_{}\".format(now), model_params = ddpg_params_tuning)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ddpg/policies.py:136: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.Dense instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:449: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:449: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ddpg/ddpg.py:94: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ddpg/ddpg.py:444: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:432: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "begin_total_asset:100000\n",
            "end_total_asset:99995.7058569457\n",
            "total_reward:-4.294143054299639\n",
            "total_cost:  0.17149700678428698\n",
            "total_trades:  10\n",
            "Sharpe:  -0.4393937514363672\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:125223.8481070335\n",
            "total_reward:25223.848107033496\n",
            "total_cost:  693.701142432137\n",
            "total_trades:  1159\n",
            "Sharpe:  0.22317075597890754\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:78872.9656911464\n",
            "total_reward:-21127.034308853603\n",
            "total_cost:  354.44841880019516\n",
            "total_trades:  270\n",
            "Sharpe:  -0.31473719368550507\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:101105.50020441337\n",
            "total_reward:1105.5002044133726\n",
            "total_cost:  158.41803119077085\n",
            "total_trades:  523\n",
            "Sharpe:  0.05295084331155536\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:92841.32190924165\n",
            "total_reward:-7158.678090758345\n",
            "total_cost:  285.19241356424914\n",
            "total_trades:  441\n",
            "Sharpe:  -0.04400567053352091\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:100098.01839813044\n",
            "total_reward:98.01839813044353\n",
            "total_cost:  317.5804545814511\n",
            "total_trades:  529\n",
            "Sharpe:  0.08613603512851835\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:92739.4599723879\n",
            "total_reward:-7260.540027612107\n",
            "total_cost:  191.08645151072778\n",
            "total_trades:  309\n",
            "Sharpe:  -0.05614602909156426\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:184718.08681328793\n",
            "total_reward:84718.08681328793\n",
            "total_cost:  413.2914850454691\n",
            "total_trades:  1474\n",
            "Sharpe:  0.468037661266039\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:348737.8082337941\n",
            "total_reward:248737.80823379412\n",
            "total_cost:  1077.897208581877\n",
            "total_trades:  2325\n",
            "Sharpe:  0.8488781572304104\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1066685.5776203808\n",
            "total_reward:966685.5776203808\n",
            "total_cost:  104.86199927912372\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0662577405642613\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:546140.3665567372\n",
            "total_reward:446140.3665567372\n",
            "total_cost:  1984.94501164637\n",
            "total_trades:  2039\n",
            "Sharpe:  1.0962526319196348\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:725392.1516885572\n",
            "total_reward:625392.1516885572\n",
            "total_cost:  1929.21672055331\n",
            "total_trades:  2367\n",
            "Sharpe:  1.0850685606375767\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1197963.7491200864\n",
            "total_reward:1097963.7491200864\n",
            "total_cost:  775.1689520095539\n",
            "total_trades:  2515\n",
            "Sharpe:  1.1368189820751509\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:742963.9653327786\n",
            "total_reward:642963.9653327786\n",
            "total_cost:  4533.239665881099\n",
            "total_trades:  2515\n",
            "Sharpe:  1.1079544079376524\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1144761.2711953667\n",
            "total_reward:1044761.2711953667\n",
            "total_cost:  3276.7738260039214\n",
            "total_trades:  2515\n",
            "Sharpe:  1.1819869523465631\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1037986.4133432165\n",
            "total_reward:937986.4133432165\n",
            "total_cost:  1362.696404204281\n",
            "total_trades:  2515\n",
            "Sharpe:  1.074102780542535\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:379800.5713001307\n",
            "total_reward:279800.5713001307\n",
            "total_cost:  561.097958557322\n",
            "total_trades:  1108\n",
            "Sharpe:  1.017939795759656\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1057570.3289890413\n",
            "total_reward:957570.3289890413\n",
            "total_cost:  230.8395712170385\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0649367921722361\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1262476.1474488103\n",
            "total_reward:1162476.1474488103\n",
            "total_cost:  3144.3996860926018\n",
            "total_trades:  2515\n",
            "Sharpe:  1.192124716528578\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1082336.6108373601\n",
            "total_reward:982336.6108373601\n",
            "total_cost:  4391.917908269401\n",
            "total_trades:  2515\n",
            "Sharpe:  1.2316074665722823\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1073915.1368067395\n",
            "total_reward:973915.1368067395\n",
            "total_cost:  3961.0404912539616\n",
            "total_trades:  2515\n",
            "Sharpe:  1.182975331285516\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:995355.5827891508\n",
            "total_reward:895355.5827891508\n",
            "total_cost:  2987.380600884907\n",
            "total_trades:  2275\n",
            "Sharpe:  1.2184826630430359\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1265155.931393874\n",
            "total_reward:1165155.931393874\n",
            "total_cost:  3421.904957608416\n",
            "total_trades:  2515\n",
            "Sharpe:  1.176230159082932\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:354591.09329952736\n",
            "total_reward:254591.09329952736\n",
            "total_cost:  2402.9491733768123\n",
            "total_trades:  1728\n",
            "Sharpe:  1.1527467961622158\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:803623.4664680425\n",
            "total_reward:703623.4664680425\n",
            "total_cost:  4972.748300329915\n",
            "total_trades:  2023\n",
            "Sharpe:  1.2292805568673504\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:955506.5108108347\n",
            "total_reward:855506.5108108347\n",
            "total_cost:  3991.8851063311604\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0580866091966117\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1155244.3521296869\n",
            "total_reward:1055244.3521296869\n",
            "total_cost:  948.3196589027705\n",
            "total_trades:  2515\n",
            "Sharpe:  1.115323871625628\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:558497.3118469787\n",
            "total_reward:458497.31184697873\n",
            "total_cost:  764.4295498483248\n",
            "total_trades:  2160\n",
            "Sharpe:  0.9180767731547095\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1066247.2705421762\n",
            "total_reward:966247.2705421762\n",
            "total_cost:  1693.194371330125\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0701861305505607\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1182423.7881506896\n",
            "total_reward:1082423.7881506896\n",
            "total_cost:  4612.575868804704\n",
            "total_trades:  2515\n",
            "Sharpe:  1.1897017571714126\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:352639.7791066152\n",
            "total_reward:252639.77910661523\n",
            "total_cost:  2203.071873404569\n",
            "total_trades:  1706\n",
            "Sharpe:  0.9297194480752586\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:512017.8187501993\n",
            "total_reward:412017.8187501993\n",
            "total_cost:  3237.2744638466706\n",
            "total_trades:  2074\n",
            "Sharpe:  1.2296052920172373\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1026617.409790139\n",
            "total_reward:926617.409790139\n",
            "total_cost:  2235.833171563652\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0634461951643783\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:432922.27221052325\n",
            "total_reward:332922.27221052325\n",
            "total_cost:  1965.1113230232177\n",
            "total_trades:  1676\n",
            "Sharpe:  0.9558190650202323\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1136563.8991799501\n",
            "total_reward:1036563.8991799501\n",
            "total_cost:  4048.353596072037\n",
            "total_trades:  2515\n",
            "Sharpe:  1.1567139637696162\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:457739.8968391317\n",
            "total_reward:357739.8968391317\n",
            "total_cost:  1451.009792129765\n",
            "total_trades:  1722\n",
            "Sharpe:  0.9887615430292522\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:832672.3654919548\n",
            "total_reward:732672.3654919548\n",
            "total_cost:  2254.518771357834\n",
            "total_trades:  2117\n",
            "Sharpe:  1.0499743963093453\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:903730.0291357596\n",
            "total_reward:803730.0291357596\n",
            "total_cost:  4160.4464784263955\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0537325331716016\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:868039.507615209\n",
            "total_reward:768039.507615209\n",
            "total_cost:  1324.6054822848214\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0055657486465792\n",
            "=================================\n",
            "Training time (DDPG):  7.679340577125549  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1sve9WGvsC__"
      },
      "source": [
        "### Model 3: PPO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TavJs-xXydY5",
        "outputId": "b3848efc-5771-4be4-d98d-bd71767858ab"
      },
      "source": [
        "config.PPO_PARAMS"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'ent_coef': 0.01,\n",
              " 'learning_rate': 0.00025,\n",
              " 'n_steps': 128,\n",
              " 'nminibatches': 4,\n",
              " 'timesteps': 20000,\n",
              " 'verbose': 0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hzvm6xu9ySZ0",
        "outputId": "8cab6c0e-f98c-4879-e63e-f730607399e4"
      },
      "source": [
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "ppo_params_tuning = {'n_steps':128, \n",
        "                     'nminibatches': 4,\n",
        "\t\t\t               'ent_coef':0.005, \n",
        "\t\t\t               'learning_rate':0.00025,\n",
        "\t\t\t              'verbose':0,\n",
        "\t\t\t              'timesteps':50000}\n",
        "model_ppo = agent.train_PPO(model_name = \"PPO_{}\".format(now), model_params = ppo_params_tuning)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:191: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:200: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/policies.py:116: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/input.py:25: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/policies.py:561: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.flatten instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/layers/core.py:332: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_layers.py:123: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/distributions.py:418: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ppo2/ppo2.py:190: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ppo2/ppo2.py:198: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ppo2/ppo2.py:206: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ppo2/ppo2.py:240: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ppo2/ppo2.py:242: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/base_class.py:1169: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n",
            "\n",
            "begin_total_asset:100000\n",
            "end_total_asset:467641.36933949846\n",
            "total_reward:367641.36933949846\n",
            "total_cost:  6334.431322515711\n",
            "total_trades:  2512\n",
            "Sharpe:  0.8257905133964245\n",
            "=================================\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:502: The name tf.Summary is deprecated. Please use tf.compat.v1.Summary instead.\n",
            "\n",
            "begin_total_asset:100000\n",
            "end_total_asset:598301.9358692836\n",
            "total_reward:498301.9358692836\n",
            "total_cost:  6714.914704657209\n",
            "total_trades:  2514\n",
            "Sharpe:  0.9104137553610742\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:487324.45261743915\n",
            "total_reward:387324.45261743915\n",
            "total_cost:  6694.683756348197\n",
            "total_trades:  2513\n",
            "Sharpe:  0.8778200252832747\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:376587.1472550176\n",
            "total_reward:276587.1472550176\n",
            "total_cost:  6498.996226416659\n",
            "total_trades:  2500\n",
            "Sharpe:  0.9265883206757147\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:411775.78502221894\n",
            "total_reward:311775.78502221894\n",
            "total_cost:  6672.303574431684\n",
            "total_trades:  2509\n",
            "Sharpe:  0.8663978354433025\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:443250.46347580303\n",
            "total_reward:343250.46347580303\n",
            "total_cost:  6792.764421390525\n",
            "total_trades:  2515\n",
            "Sharpe:  0.8567059823183628\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:547712.6511708717\n",
            "total_reward:447712.6511708717\n",
            "total_cost:  6901.285057676673\n",
            "total_trades:  2511\n",
            "Sharpe:  0.9463287997507608\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:534293.6779391705\n",
            "total_reward:434293.6779391705\n",
            "total_cost:  7026.2048333167895\n",
            "total_trades:  2515\n",
            "Sharpe:  0.9103397038651807\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:767260.8108358055\n",
            "total_reward:667260.8108358055\n",
            "total_cost:  6963.422003443312\n",
            "total_trades:  2515\n",
            "Sharpe:  0.9969063532868196\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:862184.7490450073\n",
            "total_reward:762184.7490450073\n",
            "total_cost:  6934.620506435971\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0262666662712374\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:877375.7041656245\n",
            "total_reward:777375.7041656245\n",
            "total_cost:  6802.841792068231\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0294698704729517\n",
            "=================================\n",
            "Training time (PPO):  0.8607302109400431  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CiBG2ZknsG73"
      },
      "source": [
        "### Model 4: TD3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PDUOfp6hiSuz",
        "outputId": "7a4009e8-d66c-4437-9fe3-2b234c3d75d6"
      },
      "source": [
        "## default hyperparameters in config file\n",
        "config.TD3_PARAMS"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'batch_size': 128,\n",
              " 'buffer_size': 50000,\n",
              " 'learning_rate': 0.0001,\n",
              " 'timesteps': 20000,\n",
              " 'verbose': 0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5nbOm-xiTUk",
        "outputId": "ff87aa60-bcd9-451d-f447-5b9733db2692"
      },
      "source": [
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "td3_params_tuning = {\n",
        "                     'batch_size': 128,\n",
        "\t\t\t               'buffer_size':200000, \n",
        "                     'learning_rate': 0.0002,\n",
        "\t\t\t               'verbose':0,\n",
        "\t\t\t               'timesteps':50000}\n",
        "model_td3 = agent.train_TD3(model_name = \"TD3_{}\".format(now), model_params = td3_params_tuning)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:191: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:200: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/td3/td3.py:131: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/input.py:25: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/td3/policies.py:124: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.flatten instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/layers/core.py:332: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_layers.py:57: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.Dense instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/td3/td3.py:164: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/td3/td3.py:194: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:449: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:449: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/td3/td3.py:209: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1375: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/td3/td3.py:226: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/td3/td3.py:237: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/td3/td3.py:240: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/base_class.py:1169: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n",
            "\n",
            "begin_total_asset:100000\n",
            "end_total_asset:783194.916711496\n",
            "total_reward:683194.916711496\n",
            "total_cost:  127.39982264986166\n",
            "total_trades:  2513\n",
            "Sharpe:  0.9552403851207222\n",
            "=================================\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:502: The name tf.Summary is deprecated. Please use tf.compat.v1.Summary instead.\n",
            "\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1066121.2912669356\n",
            "total_reward:966121.2912669356\n",
            "total_cost:  99.89596228181924\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0660874653722894\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1063729.743694697\n",
            "total_reward:963729.743694697\n",
            "total_cost:  99.89613513119843\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0653953427195073\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1066518.2358544066\n",
            "total_reward:966518.2358544066\n",
            "total_cost:  99.89629214983829\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0662214369484102\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1065045.0119016815\n",
            "total_reward:965045.0119016815\n",
            "total_cost:  99.89909517855105\n",
            "total_trades:  2515\n",
            "Sharpe:  1.065815088652915\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1062999.0138172389\n",
            "total_reward:962999.0138172389\n",
            "total_cost:  99.89964887245922\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0651515327742074\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1065917.6082543354\n",
            "total_reward:965917.6082543354\n",
            "total_cost:  99.89624699568067\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0660382190617566\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1065471.6899099227\n",
            "total_reward:965471.6899099227\n",
            "total_cost:  99.89623460817049\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0659053737589592\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1063534.2692774925\n",
            "total_reward:963534.2692774925\n",
            "total_cost:  99.89879225324523\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0652911618732424\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1063692.2985386187\n",
            "total_reward:963692.2985386187\n",
            "total_cost:  99.89799687662465\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0653376039985722\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1065424.7032004239\n",
            "total_reward:965424.7032004239\n",
            "total_cost:  99.90009177877862\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0658742534887662\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1067266.3177141147\n",
            "total_reward:967266.3177141147\n",
            "total_cost:  99.89821724662421\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0664598069577973\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1064936.576677842\n",
            "total_reward:964936.5766778421\n",
            "total_cost:  99.89590529309423\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0657453682816271\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1065630.9915904736\n",
            "total_reward:965630.9915904736\n",
            "total_cost:  99.8963184474129\n",
            "total_trades:  2515\n",
            "Sharpe:  1.065979186850944\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1067207.8211048262\n",
            "total_reward:967207.8211048262\n",
            "total_cost:  99.90004780527538\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0664609407461128\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1064359.181944365\n",
            "total_reward:964359.1819443649\n",
            "total_cost:  99.89558501765357\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0655484525538177\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1066272.2340616402\n",
            "total_reward:966272.2340616402\n",
            "total_cost:  99.89845281498104\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0661531172189203\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1066083.8931924317\n",
            "total_reward:966083.8931924317\n",
            "total_cost:  99.89962503281133\n",
            "total_trades:  2515\n",
            "Sharpe:  1.0661084827580432\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1061712.2587842334\n",
            "total_reward:961712.2587842334\n",
            "total_cost:  99.89594512666129\n",
            "total_trades:  2515\n",
            "Sharpe:  1.064685893904829\n",
            "=================================\n",
            "Training time (DDPG):  4.38727118174235  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oDk2qrlTLZCp"
      },
      "source": [
        "### Model 5: SAC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGzSevCzLcsR",
        "outputId": "3bb5fc43-3a30-4114-80e8-582b67774860"
      },
      "source": [
        "## default hyperparameters in config file\n",
        "config.SAC_PARAMS"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'batch_size': 64,\n",
              " 'buffer_size': 100000,\n",
              " 'ent_coef': 'auto_0.1',\n",
              " 'learning_rate': 0.0001,\n",
              " 'learning_starts': 100,\n",
              " 'timesteps': 50000,\n",
              " 'verbose': 0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1JZA0RTpIW6h",
        "outputId": "0493c23b-4a5f-4dec-828f-bb14751dc70d"
      },
      "source": [
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "sac_params_tuning={\n",
        "  'batch_size': 64,\n",
        " 'buffer_size': 100000,\n",
        "  'ent_coef':'auto_0.1',\n",
        " 'learning_rate': 0.0001,\n",
        " 'learning_starts':200,\n",
        " 'timesteps': 50000,\n",
        " 'verbose': 0}\n",
        "model_sac = agent.train_SAC(model_name = \"SAC_{}\".format(now), model_params = sac_params_tuning)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/sac/policies.py:63: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "begin_total_asset:100000\n",
            "end_total_asset:628197.7965312647\n",
            "total_reward:528197.7965312647\n",
            "total_cost:  161.17551531590826\n",
            "total_trades:  2493\n",
            "Sharpe:  0.8786969593304516\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "begin_total_asset:100000\n",
            "end_total_asset:1077672.4272528668\n",
            "total_reward:977672.4272528668\n",
            "total_cost:  99.89875688362122\n",
            "total_trades:  2515\n",
            "Sharpe:  1.069666896575114\n",
            "=================================\n",
            "Training time (SAC):  5.726297716299693  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KE1Xm9N9TnTn"
      },
      "source": [
        "### Trading\n",
        "* we use the environment class we initialized at 5.3 to create a stock trading environment\n",
        "* Assume that we have $100,000 initial capital at 2019-01-01. \n",
        "* We use the trained model of PPO to trade AAPL."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "id": "kLFUwxhCoHN_",
        "outputId": "693cc5db-1057-46dc-f540-c44668ea22fa"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>kdjk</th>\n",
              "      <th>open_2_sma</th>\n",
              "      <th>boll</th>\n",
              "      <th>close_10.0_le_5_c</th>\n",
              "      <th>wr_10</th>\n",
              "      <th>dma</th>\n",
              "      <th>trix</th>\n",
              "      <th>daily_return</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>1.259187</td>\n",
              "      <td>1.311140</td>\n",
              "      <td>1.273863</td>\n",
              "      <td>38.249401</td>\n",
              "      <td>-0.796071</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-4.623404</td>\n",
              "      <td>-1.879233</td>\n",
              "      <td>-1.130976</td>\n",
              "      <td>0.936711</td>\n",
              "      <td>1.584543</td>\n",
              "      <td>1.296077</td>\n",
              "      <td>1.535902</td>\n",
              "      <td>-0.509749</td>\n",
              "      <td>-1.478527</td>\n",
              "      <td>-5.464510</td>\n",
              "      <td>-2.953890</td>\n",
              "      <td>-0.003511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>1.042136</td>\n",
              "      <td>1.052042</td>\n",
              "      <td>1.028504</td>\n",
              "      <td>34.439476</td>\n",
              "      <td>0.080554</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-5.020681</td>\n",
              "      <td>-2.419562</td>\n",
              "      <td>-1.918796</td>\n",
              "      <td>1.654691</td>\n",
              "      <td>1.409904</td>\n",
              "      <td>1.151332</td>\n",
              "      <td>1.493971</td>\n",
              "      <td>-0.509749</td>\n",
              "      <td>-0.930227</td>\n",
              "      <td>-5.623870</td>\n",
              "      <td>-2.960046</td>\n",
              "      <td>-6.012728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>1.053078</td>\n",
              "      <td>1.107887</td>\n",
              "      <td>1.064615</td>\n",
              "      <td>35.909672</td>\n",
              "      <td>-0.447708</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-5.020959</td>\n",
              "      <td>-2.056112</td>\n",
              "      <td>-1.570256</td>\n",
              "      <td>1.202432</td>\n",
              "      <td>1.418700</td>\n",
              "      <td>1.048269</td>\n",
              "      <td>1.466007</td>\n",
              "      <td>-0.509749</td>\n",
              "      <td>-1.218098</td>\n",
              "      <td>-5.592501</td>\n",
              "      <td>-2.968936</td>\n",
              "      <td>2.474654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-07</td>\n",
              "      <td>1.136039</td>\n",
              "      <td>1.113412</td>\n",
              "      <td>1.106746</td>\n",
              "      <td>35.829746</td>\n",
              "      <td>-0.509560</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-4.980654</td>\n",
              "      <td>-2.067081</td>\n",
              "      <td>-1.415251</td>\n",
              "      <td>1.157374</td>\n",
              "      <td>1.417011</td>\n",
              "      <td>1.095224</td>\n",
              "      <td>1.439656</td>\n",
              "      <td>-0.509749</td>\n",
              "      <td>-1.211709</td>\n",
              "      <td>-5.483187</td>\n",
              "      <td>-2.973126</td>\n",
              "      <td>-0.204331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-08</td>\n",
              "      <td>1.153148</td>\n",
              "      <td>1.172415</td>\n",
              "      <td>1.159309</td>\n",
              "      <td>36.512772</td>\n",
              "      <td>-0.731694</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-4.775895</td>\n",
              "      <td>-1.900079</td>\n",
              "      <td>-1.162272</td>\n",
              "      <td>0.676332</td>\n",
              "      <td>1.480427</td>\n",
              "      <td>1.145263</td>\n",
              "      <td>1.422207</td>\n",
              "      <td>-0.509749</td>\n",
              "      <td>-1.394265</td>\n",
              "      <td>-5.247375</td>\n",
              "      <td>-2.945117</td>\n",
              "      <td>1.065455</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date      open      high  ...       dma      trix  daily_return\n",
              "0  2019-01-02  1.259187  1.311140  ... -5.464510 -2.953890     -0.003511\n",
              "1  2019-01-03  1.042136  1.052042  ... -5.623870 -2.960046     -6.012728\n",
              "2  2019-01-04  1.053078  1.107887  ... -5.592501 -2.968936      2.474654\n",
              "3  2019-01-07  1.136039  1.113412  ... -5.483187 -2.973126     -0.204331\n",
              "4  2019-01-08  1.153148  1.172415  ... -5.247375 -2.945117      1.065455\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g1KlgP0FTnTn"
      },
      "source": [
        "# create trading env\n",
        "env_trade, obs_trade = env_setup.create_env_trading(data = trade,\n",
        "                                                    env_class = SingleStockEnv) "
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bLHl6V7eqV6_",
        "outputId": "b790f0bc-23e4-4d46-8415-e76f2bfa27c2"
      },
      "source": [
        "## make a prediction and get the account value change\n",
        "df_account_value, df_actions = DRLAgent.DRL_prediction(model=model_td3,\n",
        "                                           test_data = trade,\n",
        "                                           test_env = env_trade,\n",
        "                                           test_obs = obs_trade)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "begin_total_asset:100000\n",
            "end_total_asset:308768.3018266945\n",
            "total_reward:208768.30182669451\n",
            "total_cost:  99.89708306503296\n",
            "total_trades:  439\n",
            "Sharpe:  1.9188345294206783\n",
            "=================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYXxFzD5TnTw"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtesting Performance\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GwqOO-v1NVz"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C9cpPm9YYxHC",
        "outputId": "4f4396fd-c809-411b-d70d-9f66b561f264"
      },
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "perf_stats_all = BackTestStats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
        "perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "annual return:  104.80443553947256\n",
            "sharpe ratio:  1.9188345294206783\n",
            "Annual return          0.907331\n",
            "Cumulative returns     2.087683\n",
            "Annual volatility      0.374136\n",
            "Sharpe ratio           1.918835\n",
            "Calmar ratio           2.887121\n",
            "Stability              0.909127\n",
            "Max drawdown          -0.314268\n",
            "Omega ratio            1.442243\n",
            "Sortino ratio          2.903654\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.049744\n",
            "Daily value at risk   -0.044288\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y4Gw3HNr1TDU"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MA_8LuZE1J3X",
        "outputId": "c0338cb3-0cb5-40f0-f92e-cc4f1c6997b5"
      },
      "source": [
        "print(\"==============Compare to AAPL itself buy-and-hold===========\")\n",
        "%matplotlib inline\n",
        "BackTestPlot(account_value=df_account_value, baseline_ticker = 'AAPL')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to AAPL itself buy-and-hold===========\n",
            "annual return:  104.80443553947256\n",
            "sharpe ratio:  1.9188345294206783\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (440, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-03</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-09-29</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>20</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>91.014%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>208.768%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>37.414%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>1.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>2.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-31.427%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>2.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>-0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>6.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>1.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-4.429%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31.43</td>\n",
              "      <td>2020-02-12</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>2020-06-03</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.38</td>\n",
              "      <td>2020-09-01</td>\n",
              "      <td>2020-09-18</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.84</td>\n",
              "      <td>2019-05-03</td>\n",
              "      <td>2019-06-03</td>\n",
              "      <td>2019-07-31</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.25</td>\n",
              "      <td>2019-07-31</td>\n",
              "      <td>2019-08-05</td>\n",
              "      <td>2019-08-21</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.84</td>\n",
              "      <td>2020-07-20</td>\n",
              "      <td>2020-07-24</td>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qSFMdgCJE4O-"
      },
      "source": [
        "<a id='6.3'></a>\n",
        "## 7.3 Baseline Stats"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MpR7aQIwqdC4",
        "outputId": "1ef1b94a-6a91-4f72-ff21-7d1da66e323b"
      },
      "source": [
        "print(\"==============Get Baseline Stats===========\")\n",
        "baesline_perf_stats=BaselineStats('AAPL')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (440, 7)\n",
            "Annual return          0.868103\n",
            "Cumulative returns     1.977654\n",
            "Annual volatility      0.384009\n",
            "Sharpe ratio           1.825350\n",
            "Calmar ratio           2.762260\n",
            "Stability              0.909223\n",
            "Max drawdown          -0.314273\n",
            "Omega ratio            1.416301\n",
            "Sortino ratio          2.709220\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.067808\n",
            "Daily value at risk   -0.045599\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vSFRXQfYFTQf",
        "outputId": "47e9fbfa-882a-4658-9cd7-7fa70fdbd8a8"
      },
      "source": [
        "print(\"==============Get Baseline Stats===========\")\n",
        "baesline_perf_stats=BaselineStats('^GSPC')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (440, 7)\n",
            "Annual return          0.176845\n",
            "Cumulative returns     0.328857\n",
            "Annual volatility      0.270644\n",
            "Sharpe ratio           0.739474\n",
            "Calmar ratio           0.521283\n",
            "Stability              0.339596\n",
            "Max drawdown          -0.339250\n",
            "Omega ratio            1.174869\n",
            "Sortino ratio          1.015508\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.659621\n",
            "Daily value at risk   -0.033304\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tunrA4mjE9la"
      },
      "source": [
        "<a id='6.4'></a>\n",
        "## 7.4 Compare to Stock Market Index"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5Ca2gHxi1gzX",
        "outputId": "211f1f7b-4420-41b5-bfde-a94eb6fb6177"
      },
      "source": [
        "print(\"==============Compare to S&P 500===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "BackTestPlot(df_account_value, baseline_ticker = '^GSPC')"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to S&P 500===========\n",
            "annual return:  104.80443553947256\n",
            "sharpe ratio:  1.9188345294206783\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (440, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-03</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-09-29</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>20</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>91.014%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>208.768%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>37.414%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>1.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>2.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-31.427%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>2.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>-0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>6.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>1.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-4.429%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>1.13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>31.43</td>\n",
              "      <td>2020-02-12</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>2020-06-03</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.38</td>\n",
              "      <td>2020-09-01</td>\n",
              "      <td>2020-09-18</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.84</td>\n",
              "      <td>2019-05-03</td>\n",
              "      <td>2019-06-03</td>\n",
              "      <td>2019-07-31</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.25</td>\n",
              "      <td>2019-07-31</td>\n",
              "      <td>2019-08-05</td>\n",
              "      <td>2019-08-21</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.84</td>\n",
              "      <td>2020-07-20</td>\n",
              "      <td>2020-07-24</td>\n",
              "      <td>2020-07-31</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3_aHGEOTCluG"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}